Instructions to use reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF:Q4_K_M

Use Docker

docker model run hf.co/reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF with Ollama:
```
ollama run hf.co/reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF:Q4_K_M
```

Unsloth Studio

How to use reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF to start chatting

How to use reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF with Docker Model Runner:
```
docker model run hf.co/reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF:Q4_K_M
```

Lemonade

How to use reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DistilQwen3-1.7B-uncensored-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

DistilQwen3-1.7B-uncensored-GGUF

By Convergent Intelligence LLC: Research Division

Convergent Intelligence Portfolio

Part of the DistilQwen3 Series by Convergent Intelligence LLC: Research Division

Mathematical Foundations

This is a GGUF-quantized variant. The mathematical foundations (Discrepancy Calculus, Topological Knowledge Distillation) are documented in the source model's card. The discrepancy operator $Df(x)$ and BV decomposition that inform the training pipeline are preserved through quantization — the structural boundaries detected by DISC during training are baked into the weights, not dependent on precision.

Related Models

Model	Downloads	Format
DistilQwen3-1.7B-uncensored	148	HF

Top Models from Our Lab

Model	Downloads
Qwen3-1.7B-Thinking-Distil	501
LFM2.5-1.2B-Distilled-SFT	342
Qwen3-1.7B-Coder-Distilled-SFT	302
Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF	203
Qwen3-1.7B-Coder-Distilled-SFT-GGUF	194

Total Portfolio: 41 models | 2,781 total downloads

Last updated: 2026-03-28 12:55 UTC

From the Convergent Intelligence Portfolio

DistilQwen Collection — Our only BF16 series. Proof-weighted distillation from Qwen3-30B-A3B → 1.7B and 0.6B on H100. Three teacher variants (Instruct, Thinking, Coder), nine models, 2,788 combined downloads. The rest of the portfolio proves structure beats scale on CPU. This collection shows what happens when you give the methodology real hardware.

Top model: Qwen3-1.7B-Coder-Distilled-SFT — 508 downloads

Full methodology: Structure Over Scale (DOI: 10.57967/hf/8165)

Convergent Intelligence LLC: Research Division